def test_predict_consistent_structured():
# Check binary predict decision has also predicted probability above 0.5.
X = ["A", "AB", "B"]
y = np.array([True, False, True])
kernel = MiniSeqKernel(baseline_similarity_bounds="fixed")
gpc = GaussianProcessClassifier(kernel=kernel).fit(X, y)
assert_array_equal(gpc.predict(X), gpc.predict_proba(X)[:, 1] >= 0.5)
def test_gpr_interpolation_structured():
# Test the interpolating property for different kernels.
kernel = MiniSeqKernel(baseline_similarity_bounds="fixed")
X = ["A", "B", "C"]
y = np.array([1, 2, 3])
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_pred, y_cov = gpr.predict(X, return_cov=True)
assert_almost_equal(
kernel(X, eval_gradient=True)[1].ravel(), (1 - np.eye(len(X))).ravel())
assert_almost_equal(y_pred, y)
assert_almost_equal(np.diag(y_cov), 0.0)